Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

A Two-Step Approach for Classification in Alzheimer’s Disease

The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF–THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer’s disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one

Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained

About the discovery of an explicit model to monitor blood pressure in a non-invasive way

International audienc

About the discovery of an explicit model to monitor blood pressure in a non-invasive way

Purpose: The objective of the study was to identify an explicit model to indirectly monitor blood pressure by using the electrocardiography and heart rate variability parameters, and the plethysmography. Those latter signals can be monitored through wearable non-invasive sensors, namely an electrocardiography sensor and a finger pulse oximeter sensor. The developed model was included in a real-time mobile monitoring system to realize a wearable continuous non-invasive arterial pressure monitor. Context: Continuous non-invasive arterial pressure methods can be used to continuously measure arterial blood pressure in real time and without any need for patient's body cannulation. Currently there is a high request for accurate and easy-to-use continuous non-invasive arterial pressure systems. Consequently, an increasing focus on these devices exists. Several non-invasive approaches to blood pressure have been attempted, some of which are described in [1]. Methods: The explicit model was developed under the form of a function by combining heart rate variability parameters and plethysmography measurements. We decided to avail ourselves of a Genetic Programming technique for this regression problem because it can automatically find an explicit model for the relationship between the independent variables and a dependent one, in this case one between the systolic and the diastolic blood pressure values. Therefore, once hypothesized the existence of a nonlinear relationship between heart activity, and thus electrocardiography and heart rate variability parameters, plethysmography and blood pressure values, and chosen a fitness function, we found, from among the huge number of possible models, the one that best describes the fundamental features of this relationship

Guest Editorial: enabling technologies for next generation telehealthcare

The papers in this special focus on enabling technologies for next generation telehealthcare applications. The use of Information and Communication Technology (ICT) for health and well-being is rapidly increasing in the majority of high-income countries. The interest about telehealthcare allows the provisioning of various kinds of health-related services and applications over the Internet. There are several benefits associated with tele-healthcare, including: the reduction of infection risk due to optimized patients access to clinical centers; optimized healthcare workflows; containment of hospital costs; increased patient safety; improves in the quality of life of both patients and their families. Common telehealthcare applications include tele-nursing, tele-rehabilitation, tele-dialog, tele-monitoring, tele-analysis, tele-pharmacy, tele-care, tele-psychiatry, tele-radiology, tele-pathology, teledermatology, tele-dentistry, tele-audiology, tele-ophthalmology, etc. In the past ten years, key enabling technologies (KETs) such as Internet of Things (IoT), tools for big data management and processing, Cloud/Edge/Fog computing, Artificial Intelligence (AI), Blockchain reached an advanced maturity, and therefore the potential for revolutionizing the whole tele-healthcare sector

Investigating surrogate-assisted cooperative coevolution for large-Scale global optimization

Problems involving large-scale global optimization (LSGO) are becoming more and more frequent. For this reason, the last few years have seen an increasing number of researchers interested in improving optimization metaheuristics in such a way as to cope effectively with high-dimensional search domains. Among the techniques to enhance scalability, one of the most studied is Cooperative Coevolution (CC), an effective divide-and-conquer strategy for decomposing a large-scale problem into lower-dimensional subcomponents. However, despite the progress made in the LSGO field, one of such optimizations can still require a very high number of objective function evaluations. Therefore, when the evaluation of a candidate solution requires complex calculations, LSGO can become a challenging task. Nonetheless, to date few studies have investigated the application of optimization metaheuristics to objective functions that are simultaneously high-dimensional and computationally significant. To address such a research issue, this article investigates a surrogate-assisted CC (SACC) optimizer, in which fitness surrogates are exploited within the low-dimensional subcomponents resulting from the problem decomposition. The SACC algorithm is investigated on a rich test-bed composed of 1000-dimensional problems. According to the results, SACC is able to significantly boost the convergence of the CC optimizer, leading in many cases to a relevant computational gain. (C) 2019 Elsevier Inc. All rights reserved

An asynchronous adaptive multi-population model for distributed differential evolution

In this paper a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution (AsAMP-dDE) algorithm is proposed. The distributed algorithm, following the stepping-stone model, is characterized by an asynchronous mechanism for the migration and for a multipopulation recombination employed to exchange information. The adaptive procedure is based on two steps. Firstly a local performance measure related to the average fitness improvement for each subpopulation is computed. Secondly, a specific updating scheme based on these measures takes place to randomly update the control parameter values. The asynchronous migration mechanism and the adaptive procedure allow reducing the number of control parameters to be set in the distributed model. AsAMP-dDE has been tested on the benchmarks of the CEC2016 real parameter single objective competition without adopting any specific mechanism opportunely tailored for solving such test problems. The results show that this algorithm allows obtaining good performance in most of the investigated benchmarks

Accurate estimate of Blood Glucose through Interstitial Glucose by Genetic Programming

Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach